Proteomic liquid chromatography-mass spectrometry (hereinafter referred to as “LC-MS”) approaches today combined with genome-annotated database allow to identify thousands of proteins from a protein mixture solution [1]. These approaches have been also applied to relative quantitation using stable isotope labeling [2-4]. Recently, not only comprehensive quantitation studies between two states [5,6], but also interaction analysis between protein-protein [7,8], protein-peptides [9] and protein-drug [10] have been extensively reported. So far, however, a comprehensive approach for protein contents in one sample solution has not been established yet. Protein concentrations are one of the most basic and important parameters in quantitative proteomics because the kinetics/dynamics of cellular proteins are described as changes in concentrations of proteins in particular regions. In addition, protein concentrations in a sample can be also used for relative quantitation between two samples even when the difference in concentration is too large to perform isotope-based relative; quantitation. So far, isotope-labeled synthetic peptides were used as internal standards for absolute quantitation of particular proteins of interest [11,12]. This approach would be applicable to comprehensive analysis but the cost of isotope-labeled peptides as well as the difficulty to do quantitative digestion of proteins in gel would cause a problem [13].
Even a single analysis of nanoLC-MS/MS generates a long list of identified proteins easily with the help of database searching, and additional information is extracted from this list with raw data, such as hit ranking in identification, the probability score, the number of identified peptides per protein and ion counts of identified peptides, LC retention times, and so on. Qualitatively, some parameters such as the hit rank, the score and the number of peptides per protein [14] would be a kind of indicators for protein abundance in the analyzed sample. Among them, ion counts of peptides would be the most direct parameter to describe the abundance and were used for protein expression at different states [15]. However, a mass spectrometer as a detector is not so versatile as an absorbance detector in terms of the limited linearity and the ionization suppression effect with background [16]. Therefore, it is required to normalize these parameters to obtain reliable quantitative information. The first approach along this strategy was, as far as the present inventor knows, to use the number of peptides per proteins normalized by theoretical number of peptides, which was named protein abundance index (hereinafter referred to as “PAI”), and was applied to human spliceosome complex analysis [17]. Similar concept was recently reported that the number of peptides or spectra counts in LC/LC-MS/MS analysis were used for relative quantitation [18]. The present inventor also developed normalized ion counts-based approach, where at least three peptides are used to calculate the average ion counts of each protein [19]. This approach has been used for relative quantitation in peptide correlation profiling [20].